Hash-based accessing of geometry occupancy information for point cloud coding

ABSTRACT

A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. Hash elements corresponding to one or more neighboring nodes associated with a current node are identified. A size of a hash table is decreased based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes. The data corresponding to the point cloud is decoded based on the hash table having the decreased size.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority based on U.S. Provisional ApplicationNos. 63/034,113 (filed Jun. 3, 2020) and 63/066,103 (filed Aug. 14,2020), the entirety of which are incorporated by reference herein.

BACKGROUND

This disclosure relates generally to field of data processing, and moreparticularly to point clouds.

Point cloud coding has been widely used in recent years. For example, itis used in autonomous driving vehicles for object detection andlocalization; it is also used in geographic information systems (GIS)for mapping, and used in cultural heritage to visualize and archivecultural heritage objects and collections, etc. Point clouds contain aset of high dimensional points, typically of three dimensional (3D),each including 3D position information and additional attributes such ascolor, reflectance, etc. They can be captured using multiple cameras anddepth sensors, or Lidar in various setups, and may be made up ofthousands up to billions of points to realistically represent theoriginal scenes. Compression technologies are needed to reduce theamount of data required to represent a point cloud for fastertransmission or reduction of storage. ISO/IEC MPEG (JTC 1/SC 29/WG 11)has created an ad-hoc group (MPEG-PCC) to standardize the compressiontechniques for static or dynamic point clouds.

SUMMARY

Embodiments relate to a method, system, and computer readable medium fordecoding point cloud data. According to one aspect, a method fordecoding point cloud data is provided. The method may include receivingdata corresponding to a point cloud. Hash elements corresponding to oneor more neighboring nodes associated with a current node are identified.A size of a hash table is decreased based on deleting one or more of thehash elements corresponding to non-border regions of the one or moreneighboring nodes. The data corresponding to the point cloud is decodedbased on the hash table having the decreased size.

According to another aspect, a computer system for decoding point clouddata is provided. The computer system may include one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, whereby the computer system is capable ofperforming a method. The method may include receiving data correspondingto a point cloud. Hash elements corresponding to one or more neighboringnodes associated with a current node are identified. A size of a hashtable is decreased based on deleting one or more of the hash elementscorresponding to non-border regions of the one or more neighboringnodes. The data corresponding to the point cloud is decoded based on thehash table having the decreased size.

According to yet another aspect, a computer readable medium for decodingpoint cloud data is provided. The computer readable medium may includeone or more computer-readable storage devices and program instructionsstored on at least one of the one or more tangible storage devices, theprogram instructions executable by a processor. The program instructionsare executable by a processor for performing a method that mayaccordingly include receiving data corresponding to a point cloud. Hashelements corresponding to one or more neighboring nodes associated witha current node are identified. A size of a hash table is decreased basedon deleting one or more of the hash elements corresponding to non-borderregions of the one or more neighboring nodes. The data corresponding tothe point cloud is decoded based on the hash table having the decreasedsize.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2A is a diagram of an octree structure for point cloud data,according to at least one embodiment;

FIG. 2B is a diagram of an octree partition for point cloud data,according to at least one embodiment;

FIG. 3 is a diagram of hash shrinking while maintaining boundaryelements, according to at least one embodiment;

FIG. 4 is an operational flowchart illustrating the steps carried out bya program that decodes point cloud data, according to at least oneembodiment;

FIG. 5 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, according to at leastone embodiment; and

FIG. 7 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 6, according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to point clouds. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, optimize hash-based accessing of occupancy data for pointcloud coding. Therefore, some embodiments have the capacity to improvethe field of computing by allowing for improved point cloud codingthrough the shrinking of hash data while maintaining boundary elements.

As previously described, point cloud coding been widely used in recentyears. For example, it is used in autonomous driving vehicles for objectdetection and localization; it is also used in geographic informationsystems (GIS) for mapping, and used in cultural heritage to visualizeand archive cultural heritage objects and collections, etc. Point cloudscontain a set of high dimensional points, typically of three dimensional(3D), each including 3D position information and additional attributessuch as color, reflectance, etc. They can be captured using multiplecameras and depth sensors, or Lidar in various setups, and may be madeup of thousands up to billions of points to realistically represent theoriginal scenes. Compression technologies are needed to reduce theamount of data required to represent a point cloud for fastertransmission or reduction of storage. ISO/IEC MPEG (JTC 1/SC 29/WG 11)has created an ad-hoc group (MPEG-PCC) to standardize the compressiontechniques for static or dynamic point clouds.

Previously, hash mapping technique is used for storing the geometryoccupancy information. However, sometimes, hash is not cheap in terms ofimplementations, especially when storing a large amount of geometryoccupancy information in the cache memory. It may be advantageous,therefore, to optimize hash implementations for improved point cloudcoding by using updated context modeling methods of occupancy coding.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that optimizes hash-based accessing of geometryoccupancy information for point cloud coding. Referring now to FIG. 1, afunctional block diagram of a networked computer environmentillustrating a point cloud coding system 100 (hereinafter “system”) forcoding and decoding point cloud data. It should be appreciated that FIG.1 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 5 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (laaS), as discussed below withrespect to FIGS. 6 and 7. The server computer 114 may also be located ina cloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for point cloud coding anddecoding is enabled to run a Point Cloud Coding Program 116 (hereinafter“program”) that may interact with a database 112. The Point Cloud CodingProgram method is explained in more detail below with respect to FIG. 4.In one embodiment, the computer 102 may operate as an input deviceincluding a user interface while the program 116 may run primarily onserver computer 114. In an alternative embodiment, the program 116 mayrun primarily on one or more computers 102 while the server computer 114may be used for processing and storage of data used by the program 116.It should be noted that the program 116 may be a standalone program ormay be integrated into a larger point cloud coding program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1. Furthermore, two or more devices shown in FIG. 1 may beimplemented within a single device, or a single device shown in FIG. 1may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2A, a diagram of an octree structure 200A isdepicted. In TMC13, if the octree geometry codec is used, the geometryencoding proceeds as follows. First, a cubical axis-aligned bounding boxB is defined by two points (0,0,0) and (2^(M−1), 2^(M−1), 2^(M−1)),where 2^(M−1) defines the size of B and M is specified in the bitstream.The octree structure 200A is then built by recursively subdividing B. Ateach stage, a cube is subdivided into 8 sub-cubes. An 8-bit code, namelythe occupancy code, is then generated by associating a 1-bit value witheach sub-cube in order to indicate whether it contains points (i.e.,full and has value 1) or not (i.e., empty and has value 0). Only fullsub-cubes with a size greater than 1 (i.e., non-voxels) are furthersubdivided.

Referring now to FIG. 2B, a diagram of an octree partition 200B isdepicted. The octree partition 200B may include a two-level octreepartition 202 and a corresponding occupancy code 204, where cubes andnodes in dark indicate they are occupied by points. The occupancy code204 of each node is then compressed by an arithmetic encoder. Theoccupancy code 204 can be denoted as S which is an 8-bit integer, andeach bit in S indicates the occupancy status of each child node. Twoencoding methods for occupancy code 204 exist in TMC13, i.e., thebit-wise encoding and the byte-wise encoding methods, and the bit-wiseencoding is enabled by default. Either way performs arithmetic codingwith context modeling to encode the occupancy code 204, where thecontext status is initialized at the beginning of the whole codingprocess and is updated during the coding process.

For bit-wise encoding, eight bins in S are encoded in a certain orderwhere each bin is encoded by referring to the occupancy status ofneighboring nodes and child nodes of neighboring nodes, where theneighboring nodes are in the same level of current node. For byte-wiseencoding, S is encoded by referring to an adaptive look up table(A-LUT), which keeps track of the N (e.g., 32) most frequent occupancycodes and a cache which keeps track of the last different observed M(e.g., 16) occupancy codes.

A binary flag indicating whether S is the A-LUT or not is encoded. If Sis in the A-LUT, the index in the A-LUT is encoded by using a binaryarithmetic encoder. If S is not in the A-LUT, then a binary flagindicating whether S is in the cache or not is encoded. If S is in thecache, then the binary representation of its index is encoded by using abinary arithmetic encoder. Otherwise, if S is not in the cache, then thebinary representation of S is encoded by using a binary arithmeticencoder. The decoding process starts by parsing the dimensions of thebounding box B from bitstream. The same octree structure is then builtby subdividing B according to the decoded occupancy codes.

Referring now to FIG. 3, an illustration 300 of hash shrinking whilemaintaining boundary elements is depicted. The hash shrinking mayinclude hash elements 302A-C, boundary hash elements 304A-C, and acurrent node 306. According to one or more embodiments, the codedgeometry occupancy information may be saved in cache memory because thelater coded nodes need to use this information as context.

A hash table may be used to store occupancy information. For each octreepartition depth d, a hash table H_(d) is maintained, where the key isthe Morton code of an octree node in depth d, i.e., M_(i)^(d)=Morton(x_(i) ^(d), y_(i) ^(d), z_(i) ^(d)), where (x_(i) ^(d),y_(i) ^(d), z_(i) ^(d)) is the 3D coordinates of the octree node. Usingthe Morton code M_(i) ^(d) as the key, one can access its occupancyvalue in the hash table H_(d). The occupancy value of the octree node iin depth d can be obtained as follows:

$\begin{matrix}{{OC}_{i}^{d} = \left\{ \begin{matrix}{H_{d}\left( M_{i}^{d} \right)} & {{{if}\mspace{14mu}{H_{d}\left( M_{i}^{d} \right)}}!={NULL}} \\0 & {otherwise}\end{matrix} \right.} & (1)\end{matrix}$

When encoding/decoding the occupancy value of current node, theoccupancy information of neighboring nodes is obtained from the hashtable H_(d). After encoding/decoding an occupancy value of the currentnode, the coded occupancy value is then stored in H_(d). Tooptimize/accelerate the hash implementation, the hash table may be keptas small as possible, so that the cost for storing information is lessand the searching speed is faster.

In one or more embodiments, a maximum hash table size may be defined. Itmay be appreciated that the maximum size can be either fixed for allcases or can be configured differently case by case and sent in thebitstream as part of high-level syntax, such as sequence parameter set,geometry parameter set or slice header, etc. When the hash table reachesthe maximum capacity, the hash table will shrink itself by removingpartial elements in it. The rules regarding which elements are to beremoved may differ in situations. In one embodiment, the hash table willremove the elements that are not at the block boundary.

The block boundary can be defined according to the position coordinatesof current node. Usually, the block size is defined as a power of two.For example, the block size can be (2^(M), 2^(M), 2^(M)) in 3D space.Therefore, the positions with the coordinates (2^(im)−1, 2^(jM)−1,2^(kM)−1), where i, j, k=1, 2, 3, . . . , are boundaries. Therefore, allthe hash element with the key value Morton(2^(iM)−1, 2^(jM)−1, 2^(kM)−1)are kept and all the rest elements can be deleted from the table. Thus,boundary hash elements 304A-C may be are at boundaries, and the hashelements 302A-C that may not belong to boundaries, i.e., non-borderregions, can be removed. Note that the boundary size M can be eitherfixed for all cases or can be configured differently case by case andsent in the bitstream as part of high-level syntax, such sequenceparameter set, geometry parameter set or slice header, etc. However, insome embodiments, if parent-level context information is utilized, thehash shrinking may not be used to achieve the best performance. If theparent-level context is not utilized, it is safe to shrink the hashtable while keeping boundary elements without performance loss.

Previously, node-based geometry coding partitioned the whole point cloudinto several sub-nodes via breadth-first octree partitioning and eachsub-node can be coded differently or in parallel. In this scheme, eachsub-node can maintain a separate hash table and each hash table fromdifferent sub-nodes is independent from each other. In this case, themaximum size of hash table is reduced as well because each sub-node is aportion of the original point cloud. However, this may introduce someperformance loss in terms of coding efficiency because contextinformation is not shared across node boundaries. To compensate theloss, in another embodiment, the elements at node boundaries aremaintained and can be used for different sub-nodes.

Referring now to FIG. 4, an operational flowchart illustrating the stepsof a method 400 carried out by a program for coding point cloud data isdepicted.

At 402, the method 400 may include receiving data corresponding to apoint cloud.

At 404, the method 400 may include identifying hash elementscorresponding to one or more neighboring nodes associated with a currentnode.

At 406, the method 400 may include decreasing a size of a hash tablebased on deleting one or more of the hash elements corresponding tonon-border regions of the one or more neighboring nodes.

At 408, the method 400 may include decoding the data corresponding tothe point cloud based on the hash table having the decreased size.

It may be appreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 5 is a block diagram 500 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 5 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 5. Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1)and the Point Cloud Coding Program 116 (FIG. 1) on server computer 114(FIG. 1) are stored on one or more of the respective computer-readabletangible storage devices 830 for execution by one or more of therespective processors 820 via one or more of the respective RAMs 822(which typically include cache memory). In the embodiment illustrated inFIG. 5, each of the computer-readable tangible storage devices 830 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 830 is asemiconductor storage device such as ROM 824, EPROM, flash memory, anoptical disk, a magneto-optic disk, a solid state disk, a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, and/or another type of non-transitory computer-readabletangible storage device that can store a computer program and digitalinformation.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1) and the Point Cloud Coding Program 116 (FIG. 1) canbe stored on one or more of the respective portable computer-readabletangible storage devices 936, read via the respective R/W drive orinterface 832 and loaded into the respective hard drive 830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1) and thePoint Cloud Coding Program 116 (FIG. 1) on the server computer 114(FIG. 1) can be downloaded to the computer 102 (FIG. 1) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the Point Cloud CodingProgram 116 on the server computer 114 are loaded into the respectivehard drive 830. The network may comprise copper wires, optical fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (laaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 6, illustrative cloud computing environment 600 isdepicted. As shown, cloud computing environment 600 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 600 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 600 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 7, a set of functional abstraction layers 700 providedby cloud computing environment 600 (FIG. 6) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments are notlimited thereto. As depicted, the following layers and correspondingfunctions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Point Cloud Coding 96. Point Cloud Coding96 may optimize hash-based accessing of occupancy data for point cloudcoding.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of decoding point cloud data, executableby a processor, comprising: receiving data corresponding to a pointcloud; identifying hash elements corresponding to one or moreneighboring nodes associated with a current node; decreasing a size of ahash table based on deleting one or more of the hash elementscorresponding to non-border regions of the one or more neighboringnodes; and decoding the data corresponding to the point cloud based onthe hash table having the decreased size.
 2. The method of claim 1,wherein a maximum size of the hash table is defined for the datacorresponding to the point cloud.
 3. The method of claim 2, wherein themaximum is signaled in a sequence parameter set, a geometry parameterset, or a slice header.
 4. The method of claim 2, wherein the hash tableis decreased by removing partial elements from the hash table based onthe hash table reaching the maximum size.
 5. The method of claim 1,wherein the hash table is decreased based on a Morton order associatedwith the hash elements.
 6. The method of claim 1, wherein a boundarysize associated with the one or more neighboring nodes is signaled in asequence parameter set, a geometry parameter set, or a slice header. 7.The method of claim 1, wherein the hash table is decreased based onparent-level context information not being used.
 8. A computer systemfor decoding point cloud data, the computer system comprising: one ormore computer-readable non-transitory storage media configured to storecomputer program code; and one or more computer processors configured toaccess said computer program code and operate as instructed by saidcomputer program code, said computer program code including: receivingcode configured to cause the one or more computer processors to receivedata corresponding to a point cloud; identifying code configured tocause the one or more computer processors to identify hash elementscorresponding to one or more neighboring nodes associated with a currentnode; decreasing code configured to cause the one or more computerprocessors to decrease a size of a hash table based on deleting one ormore of the hash elements corresponding to non-border regions of the oneor more neighboring nodes; and decoding code configured to cause the oneor more computer processors to decode the data corresponding to thepoint cloud based on the hash table having the decreased size.
 9. Thecomputer system of claim 8, wherein a maximum size of the hash table isdefined for the data corresponding to the point cloud.
 10. The computersystem of claim 9, wherein the maximum is signaled in a sequenceparameter set, a geometry parameter set, or a slice header.
 11. Thecomputer system of claim 9, wherein the hash table is decreased byremoving partial elements from the hash table based on the hash tablereaching the maximum size.
 12. The computer system of claim 8, whereinthe hash table is decreased based on a Morton order associated with thehash elements.
 13. The computer system of claim 8, wherein a boundarysize associated with the one or more neighboring nodes is signaled in asequence parameter set, a geometry parameter set, or a slice header. 14.The computer system of claim 8, wherein the hash table is decreasedbased on parent-level context information not being used.
 15. Anon-transitory computer readable medium having stored thereon a computerprogram for decoding point cloud data, the computer program configuredto cause one or more computer processors to: receive data correspondingto a point cloud; identify hash elements corresponding to one or moreneighboring nodes associated with a current node; decrease a size of ahash table based on deleting one or more of the hash elementscorresponding to non-border regions of the one or more neighboringnodes; and decode the data corresponding to the point cloud based on thehash table having the decreased size.
 16. The computer readable mediumof claim 15, wherein a maximum size of the hash table is defined for thedata corresponding to the point cloud.
 17. The computer readable mediumof claim 16, wherein the maximum is signaled in a sequence parameterset, a geometry parameter set, or a slice header.
 18. The computerreadable medium of claim 16, wherein the hash table is decreased byremoving partial elements from the hash table based on the hash tablereaching the maximum size.
 19. The computer readable medium of claim 15,wherein the hash table is decreased based on a Morton order associatedwith the hash elements.
 20. The computer readable medium of claim 15,wherein a boundary size associated with the one or more neighboringnodes is signaled in a sequence parameter set, a geometry parameter set,or a slice header.